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Constructing narrative visualizations as a means of increasing learner
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Constructing Narrative Visualizations as a means of
Increasing Learner Engagement
Bilal Yousuf
KDEG, Trinity College Dublin
Dublin, Ireland
yousufbi@scss.tcd.ie
Owen Conlan
KDEG, Trinity College Dublin
Dublin, Ireland
Owen.Conlan@scss.tcd.ie
ABSTRACT
Increasingly visualization systems are using storytelling to present
complex data. However, many approaches neglect enabling users
to independently explore details within the story. The research
presented in this paper provides an overview of the
implementation and discusses the evaluation of a novel
framework (VisEN), which aims to allow users to construct
narratives containing multiple exploration paths. The narratives
are told through dynamically generated visualization techniques,
which are personalized for individual end users, and where every
visualization technique in the narrative can be further explored.
The evaluation described assesses the role personalized visual
narratives had in increasing engagement of weaker students with
an online database SQL course. It was found that weaker students
who regularly interacted with their personalized visual narratives
showed an improvement in engagement.
Categories and Subject Descriptors
H.3.5 [Online Information Services]: Web-based services; H.5.2
[User Interface] Graphical User Interface; H.5.4 [Hypertext/
Hypermedia]: Architectures
General Terms
Design, Experimentation, Human Factors, Performance
Keywords
Visualizations, Personalized Visual Narratives, Visual Interaction
and Exploration
1. INTRODUCTION
Research in the field on Information Visualization has largely
been focused on visual analytics and exploration, whereas
research in visual presentation and storytelling has recently started
to gain momentum. Storytelling in information visualization, or
narrative as it is referred to in this work, can be defined as an
ordered sequence of steps consisting of visualizations, which are
linked or connected to make the communicated message more
memorable [1]. Stories provide effective ways of highlighting
facts, making points and passing on information [16], while
visualizations facilitate a simple means to understand digitized
data as they map data attributes to visual properties [6]. The
research addressed in this paper presents a framework, VisEN
(Visual Exploration with Narrative), which aims to provide a
novel way to extract knowledge and meaning from data. VisEN
supports users in the role of narrative composers to analyze
potentially complex data through advanced web based interfaces
to construct narratives. The narratives include explorations paths
to facilitate data drill downs and viewing related data. The
narratives are automatically transformed into personalized visual
narratives for end users, who can analyze and explore sections of
the narrative through multiple interactive visualization techniques
and gain a deep understanding of the data.
This paper discusses the implementation overview, evaluation and
preliminary results of two key components of the VisEN
framework: the Narrative Builder and the Visual Narrative
Explorer. The aim of the Narrative Builder is to enable narrative
composers to construct explorable narratives through an advanced
web–based interface, which enables the analysis of potentially
complex data without dealing with data complexity issues. The
aim of the Visual Narrative Explorer is to personalize the visual
narratives for end users and facilitate analysis and exploration of
these narratives. VisEN was deployed to the AMAS [20]
Personalized Learning Environment (PLE), to provide
personalized visual narratives to 108 students who participated in
an online SQL course. Two evaluations were completed with the
first analyzing how effective the AMAS course professor found
the user interfaces provided by the Narrative Builder to build
explorable visual narratives. The second evaluation focused on
weaker students’ level of engagement (“participation in
educationally effective practices” [17]). In particular, it analyzed
how effective the personalized visual narratives were in allowing
weaker students to extract meaning from their activity data, in
order to motivate them to engage with the course. The results of
both evaluations were very encouraging and it was found that
these learners were drawn to their visual narratives in order to
understand and improve their engagement with the course.
The remainder of this paper is structured as follows: Section 2
discusses the VisEN framework approach. Section 3 presents a
review of the related work. Section 4 describes an implementation
overview of VisEN. Section 5 presents two use cases; the first
describing a domain expert using VisEN to construct visual
narratives, and the second describing a learner using her
personalized visual narratives to gain a thorough understanding of
her personal course log data. Section 6 evaluates effectiveness of
VisEN when deployed to a PLE and discusses preliminary results.
Finally, section 7 discusses conclusions and future work.
2. VISEN APPROACH
VisEN automatically transforms narratives into explorable visual
narratives. This transformation requires data characterization and
mappings to transform data to appropriate visualization
techniques. Data characterization or data transformation [6]
involves analyzing data to facilitate automated mappings to
visualization techniques. To enable this mapping or visual
encoding [6], the affordances and characteristics of visualization
techniques are required, for example, through a matrix. VisEN
narratives consist of data slices, which are constructed using data
fields, metadata, filters and aggregations. Data slices form the
chapters or sections of the narrative.
When a data slice is constructed, visualizations that can render the
data are automatically generated and presented to the narrative
composer as a set. The narrative composer decides which
visualizations to keep in the set. This action introduces humans
into the visual matching process. This results in a refined set of
visualizations for a data slice, and takes place before the narrative
is transformed into a visual narrative. VisEN automatically
generates personalized exploration paths to allow end users to
select elements within visualizations and view details or view
related data through other visualizations. The exploration paths
are generated based on users preferences and consists of
visualizations showing details and related data to the narrative
viewed.
To complete the narrative, the narrative composer connects the
data slices to each other in a chronological order and publishes it.
Figure 1 shows a simplified view of the process used by VisEN to
produce personalized explorable visual narratives.
Figure 1: VisEN Flow
3. RELATED WORK
Interaction, exploration and visual storytelling are important
aspects of presentation in information visualization as they allow
users to gain a deeper understanding of data. This section analyses
the state of the art to determine how adequately generating
dynamic visual narratives and enabling personalized visual
explorations of these narratives have been addressed.
Visual narratives have been effectively used in journalism [9, 15,
24] to tell stories with data. These have ranged from presenting
several visualizations with annotations in one view to slides
containing interactive visualizations to tell a story. Contextifier
[15] for example, provides visualizations embedded in news
articles and provides visualizations of related articles allowing
users to navigate and explore these. Tools such as Gapminder
[22], GED Viz [8] and SketchStory [18] provide users with
interactive visual storytelling. However, the interactions are
limited to hovering the mouse over data points to reveal details
and filtering regions of the data. StoryFlow [19] allows users to
explore data in a second layer of the story through its bundling
operation, which reveals a level of detail beneath a bundled line.
Spotfire [27] provides users with data drill down capabilities,
where visual structures can be clicked by users and the system
loads another visualization that also provides a drill down of the
data. A user can choose to drill down further and view the
selected data through a further visualization. However, with drill
downs, users reach an end point where their exploration must.
Exploration paths provided by VisEN are linked to elements in
the visual narrative and when these elements are clicked,
visualization techniques are generated rendering a drill down view
or a related data view of the element selected. Drill down views
show the details surrounding a selected element, whereas related
data views show data which shares relationships with the selected
element. When a user reaches the lowest point in a drill down, she
always has the option to view related data. Visualizations have
been used in Technology Enhanced Learning (TEL) to present
student activity data and peer comparisons [11, 22] to motivate
students. However, these are not represented through visual
narratives, where users can explore the data presented.
Personalized visual narratives can aid the process of
understanding complex data as they can present personalized data
and provide visualizations that suit individual preferences. In
Tableau Story, Tableau [26] selects the most suitable visualization
for the story point and this can be changed by the analyst.
Similarly Google Fusion Tables [10] uses a suitable visualization
for the data. However, we find on many occasions, a number of
visualization techniques are suitable to render the same data. The
visualizations generated by these systems are not personalized to
end user preferences. In TEL, a number of systems [2, 3, 21]
provide personalized visualization forming part of the learning
module. VisEN’s architecture consists of a Personalization
Engine, which generates personalized exploration paths for end
users. User data preferences are stored in a user model, which are
used to personalize the exploration paths.
From the visualization tools that support visual interactions and
explorations, Spotfire [27] supports drill down explorations,
however, the exploration path is fixed and an end user has the
option to either view the details behind a data point or not. The
exploration is not independent of the path constructed by the
analyst. VisEN provides multiple exploration paths from each
data slice, allowing end users to explore various tailored paths
through the data set. Hence the exploration is independent from
one end user to another and this allows users to derive personal
conclusions.
From the analysis above, it can be seen that VisEN progresses the
state-of-the-art by introducing three novel factors which focus on
allowing end users to: 1) explore related data through exploration
paths; 2) view visual narratives; and 3) analyze tailored
exploration paths.
4. IMPLEMENTATION OVERVIEW
The VisEN architecture uses principles discussed in 1) the
visualization pipeline [6]; 2) the visual information seeking
mantra [25]; 3) the Template Editor and Shelf Configuration
visual interface design approaches [13]; and 4) sequencing in
visual narratives [14] to generate explorable personalized visual
narratives. Figure 2 shows VisEN architecture, which consists of
the Narrative Builder, the Visualization Engine and the Visual
Narrative Explorer components.
4.1 Narrative Builder
The Narrative Builder enables narrative composers to easily
construct narratives from complex data. Visualizations are not
introduced into the narrative during the narrative building phase.
Figure 2. VisEN Architecture
4.1.1 Data Connection Component
Narrative Composers use the Data Connection component to
connect to heterogeneous data sources to construct narratives.
Data connections are established by selecting data sources or
specifying connection parameters. Preconfigured data source
parameters are stored in configurations files and new data source
parameters supplied by narrative composers are also saved to
these files.
4.1.2 Data Analysis Interface
Data slices form the individual pieces of narratives and are
constructed by the narrative composers via the web based Data
Analysis Interface. In addition to constructing the data slice, the
Data Analysis Interface allows narrative composers to analyze
data sources. The interface consists of a number of buttons which
run general queries such as “select count..”, “select <field>..”
etc.; this simplifies the process of constructing narratives as the
raw data values can be analyzed by narrative composers. The Data
Analysis Interface uses the jQuery Accordion widget to show
source tables and fields and uses the jQuery Draggable widget to
facilitate dragging and dropping of data fields to construct data
slices. The interface provides a canvas with panels for fields and
filters. The data fields from the Draggable widget can be dropped
onto these panels to construct data slices. The drag and drop
design approach has been used effectively in state of the art [26].
When a field is dropped onto a filter panel, VisEN runs queries to
fetch data to allow narrative composers to specify which values to
use in the filter.
4.1.3 Encoded Exploration
An important and novel aspect of VisEN is exploration paths,
which are automatically constructed and connected to data slices.
Exploration paths consist of a series of visualizations linked to
each data slice or section of the narrative. End users can view and
analyze exploration paths by clicking on elements in a data slice
to drill down into sections of a narrative or explore related items
to obtain a deeper understanding of the data. Exploration paths
are constructed by VisEN using data slices that have common
elements or derivatives in the narrative. The narrative composer
can view the automatically constructed exploration paths and can
remove and visualization to the path via the available add/remove
options on the Data Analysis Interface.
4.2 Visualization Engine
The Visualization Engine transforms narratives into visual
narratives by mapping data slices from the narrative to
visualization techniques.
4.2.1 Query Builder
The Query Builder uses the data and metadata provided by the
narratives composers in the data slices to generate and execute
SQL queries against the specified data sources. The query results
are formatted by data type, size (data sizes and number of series of
data) and coordinates (data points) to aid the Rules Engine in
selecting appropriate visualizations for the data slice.
4.2.2 Rules Engine
The Rules Engine uses the formatted query results and the data
slice metadata to determine appropriate visualization techniques
for each data slice of the narrative. Instead of building
visualizations, VisEN utilizes JavaScript visualization libraries to
source visualization techniques. Extensive research [4, 5, 7, 12]
has evaluated the affordances and characteristics of visualization
techniques and compared the suitability of various techniques for
data sets. This research has been used by VisEN to allow
developers to build matrices that specify the characteristics,
affordances and constraints of the supported visualizations. The
matrices are stored as XML files and new visualizations can be
seamlessly incorporated into the framework by creating a new
XML file (matrix) and importing the JavaScript library.
4.2.3 Visualization Builder
The current set of visualization techniques supported by VisEN
requires data to be formatted as JSON objects. The Visualization
Builder creates JSON objects using the query results and metadata
and populates the set of visualization techniques (currently nine
techniques are supported including: bar chart, bubble chart,
gauge, line chart, pie chart, scatterplot, stacked bar chart, area
chart and parallel coordinates). It also makes the populated set of
visualizations available to the narrative composer to view through
a web interface as a dropdown list, where visualizations can be
removed from the set. The remaining set is used for the visual
narrative.
4.3 Visual Narrative Explorer
The Visual Narrative Explorer personalizes the visual narratives
for end users by generating tailored exploration paths for each
narrative based on individual preferences. It provides a web-based
interface where end users can analyze visual narratives and view
exploration paths to understand data.
4.3.1 Personalization Engine
The Encoded Exploration component generates derivatives from
data slices for exploration paths, which can be accepted or
rejected by the narrative composer. Accepted derivate data slices
and data slices related to the narrative are used to form
personalized exploration paths. The Personalization Engine
personalizes the exploration paths using user data preferences, set
in the user model. These preferences are set when end users asked
to select data tags (taken from data slice metadata) they are
interested in exploring when viewing visual narratives. Selected
tags are stored in the VisEN user model and these are used to
personalize the exploration path.
4.3.2 Narrative Dashboard
Published visual narratives are made available to end users
through the web based Narrative Dashboard. End users are
presented with the first data slice of visual narratives and the
remaining data slice can be access by clicking the titles at top of
the interface. When an end user wishes to explore an element in
the data slice, she can click it and this generates the first
visualization in exploration path, which is shown in a popup
window on the web browser. Clicking an element in the visual
narrative fires an AJAX request and the linked exploration path is
made available to the end user. At any point the end users can
close the exploration path popup window and continue analyzing
the visual narrative or alternatively continue with the exploration.
5. USE CASES
This section discusses two use cases; the first use case describes a
university professor using VisEN to construct two narratives. The
second use case describes a student using personalized visual
narratives to understand and improve her course engagement.
5.1 Use Case One – University Professor
John is a Professor lecturing Database Management System to
final year university students. His students need to use the AMAS
[20] portal to study SQL. John understands the challenges
learners’ have engaging with online learning modules and wishes
to provide visual narratives to improve engagement by allowing
them to visually analyze and explore their individual log data.
John logs into VisEN and assumes the role of a narrative
composer. He connects to the AMAS data source containing
learner log data from the last time the course was run. This data
source consists of thousands of entries with all the interactions
learners had with the course over a three months period. After
analyzing the data he wishes to construct two narratives. He starts
constructing data slices by dragging data fields onto the Narrative
Builder interface.. He clicks on the "Visualize Data" button and
views the set of visualizations for each data slice and also views
the automatically generated exploration paths. Finally he
disassociates the narrative with the previous log data and connects
it to new data source (this consists of test entries as the course is
yet to commence) and publishes the narratives.
5.2 Use Case Two – Final Year Student
Michelle is a final year Computer Science student and has
received an average grade of below 50% each year during the first
three years of her course. However, she is determined to improve
her grade in her final year. As part of one of her modules she
needs to study SQL using the AMAS portal. During the first
month of the three month module, Michelle has occasionally used
the portal. At the end of this month she receives a notification
from the portal informing her of her poor engagement with course
activities and advises her that in previous years the students who
continued to engage at this level performed poorly.
Following on from this notification, Michelle wants to understand
how she can improve her engagement and estimate how much
time she must commit to this module to perform well. She views
her personalized visual narratives and analyzes her engagement
score and how it was calculated. She analyzes peer engagement
comparisons using her visual narratives which allow her to
determine how to improve engagement. By analyzing peer
comparisons and exploring her visual narratives, Michelle is able
to predict how long it will take her to complete her next five
activities. Michelle now feels motivated and determined to work
hard and obtain a good grade. As she completes each activity, she
explores her visual narratives and estimates the time the next
activity would take.
6. EVALUATION
VisEN was deployed to the AMAS [20] PLE during the 2013-
2014 academic year to provide learners with personalized visual
narratives to allow them to analyze their engagement score, view
time spent on activities and analyze peer comparisons. AMAS
provides a dynamic and adaptive framework for composition and
assignment of personalized learning activities [20]. It has been
used over the past three years to deliver an SQL database course
to final year university students in Trinity College Dublin. Two
evaluations were carried out in conjunction with the delivery of
the AMAS SQL course. The first evaluation involved a university
professor using VisEN to construct visual narratives for his
students. The second evaluation involved participating students of
the course using personalized visual narratives in order to
understand their performance and engagement from their log data.
6.1 Evaluating the Narrative Builder
In this evaluation, the professor whose students worked through
the AMAS activities, assumed the role of a narrative composer
and constructed narratives using the AMAS log data from the
2012-2013 academic year. The aim of this trial was to evaluate the
end to end tasks of the narrative composer: analyze a complex
data set; construct narratives with exploration paths; and critique
the set of generated visualizations. The professor was provided
with a 15 minutes training session on how to use the Narrative
Builder and then asked to construct the two narratives using the
Narrative Builder (shown on the left of figure 3): 1) A narrative
showing learners’ engagement score and how it was calculated; 2)
A narrative presenting the time learners spent on activities, and
allowing learners to compare activity times with their peers.
Exploration paths were automatically generated, which showed a
breakdown of selected students' engagement score (drill down).
The other exploration path showed engagement scores of similar
students (related data). Once both narratives were completed
(which took 25 minutes with some assistance), the professor was
asked to interact with the visual narratives, which were
automatically generated and analyze the data through exploration
paths. During the analysis, he was asked to answer questions by
exploring and interacting with the visual narratives, which he did
with ease and answered all the questions.
His final task was to critique the visualizations and the process of
constructing the narrative through a questionnaire and interview.
The questionnaire focused on how useful the professor found the
process of constructing narratives and analyzing exploration
paths. For example, one of the questions asked: "When viewing
course engagement by activity, how useful was it to view students
with similar engagement through an exploration path”. The
questions also addressed how well the framework and
visualizations met his needs, such as “Did the framework support
you in telling the story you wanted to tell” and “Where you ever
frustrated with the limitations of the user interface”, to which he
offered useful suggestions such as providing tooltips and help
options. From the feedback the professor found exploration paths
very useful for gaining insight and was able to tell the story
Figure 3. Narrative Builder Interface (left) and two sample visualizations from a Personalized Student Visual Narrative (Right)
requested. He expressed that the data slices and resulting
visualizations represented his needs quite well. In the interview,
the professor expressed that he was able to follow and interact
with the visualizations easily and expressed confidence in
constructing data slices and building the narratives. Examining
the time taken to learn and construct the narratives, it was evident
that the professor had a very positive experience constructing
narratives using the Narrative Builder.
6.2 Evaluating Personalized Visual Narratives
One of the primary aims of AMAS is to support weaker students
completing their course. The second evaluation focused on
analyzing the impact the personalized visual narratives had on
supporting weaker learners to improve course engagement. The
right hand side of Figure 3 shows two visualizations from one of
the narratives presented to learners. 108 students participated in
the AMAS SQL course; 22 of these were identified as weak
students as they had an average grade of below 50% for each of
the previous three years of their course.
During the course, AMAS sent fortnightly notifications to learners
informing them of their engagement levels. The first study
analyzed the AMAS log data, (consisting of thousands of entries
for three months of interactions from 108 learners), and found that
all of the weaker students had at some stage received a below
average engagement notification. The analysis of the log data of
the 22 weaker students found that 17 of these students showed an
improvement in engagement following this notification. It was
found that 14 of these 17 learners were immediately drawn to
their personalized visual narrative following a below average
engagement notification. All of these 14 learners executed a
minimum of 45% of their total narrative interactions on the first
day after reading the notification. Following this notification
(which did not explicitly direct them to their personalized visual
narratives), these learners frequently returned to view their
personalized visual narratives. Hence, it can be concluded that the
personalized visual narratives assisted these learners in gaining a
deeper knowledge of their performance data.
The second study analyzed if there was a correlation between
weaker students interacting with their visual narratives and an
improvement in engagement. The log data of the 17 weaker
students, who showed engagement improvement following a
below average engagement notification, was analyzed. It was
found that all of these learners showed a minimum of a 70%
increase in interactions with their visual narratives during the
period in which their engagement improved. From this, it was
concluded that weaker students who increased in interactions with
their personalized visual narratives showed an improvement in
their course engagement level.
7. CONCLUSIONS AND FUTURE WORK
This paper introduced VisEN as a framework to construct visual
narratives and facilitate personalized visual explorations by
allowing end users to: 1) explore related data; 2) analyze visual
narratives; and 3) analyze personalized exploration paths.
Two evaluations were carried out; the first evaluation involved a
university professor analyzing the log data of his students' course
activities and constructing visual narratives. The results of this
evaluation were positive, with the professor confidently creating
data slices and narratives and positively commenting on his
experience of executing the tasks required. The second evaluation
involved analyzing the log data of weaker students who
participated in an online SQL course. This evaluation found that
the personalized visual narratives assisted these learners in
understanding and improving their engagement and performance
data.
Preliminary results have been obtained from both evaluations.
Further work is required to evaluate the Narrative Builder through
qualitative and quantitative analysis using several users. In the
2014 - 2015 academic year, it is intended to continue to provide
learners with personalized visual narratives and compare
engagement results with control groups, and quantify the increase
in engagement levels, and verify the statistical significance.
8. ACKNOWLEDGMENTS
This research is supported by the Science Foundation Ireland
(Grant 12/CE/I2267) as part of CNGL (www.cngl.ie) at Trinity
College Dublin.
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